Atmospheric levels of reactive
nitrogen have increased substantially during the last century resulting in
increased nitrogen deposition to ecosystems, causing harmful effects such as
soil acidification, reduction in plant biodiversity and eutrophication in
lakes and the ocean. Recent developments in the use of atmospheric remote
sensing enabled us to resolve concentration fields of NH3 with larger
spatial coverage. These observations may be used to improve the
quantification of NH3 deposition. In this paper, we use a relatively
simple, data-driven method to derive dry deposition fluxes and surface
concentrations of NH3 for Europe and for the Netherlands. The aim of
this paper is to determine the applicability and the limitations of this
method for NH3. Space-born observations of the Infrared Atmospheric
Sounding Interferometer (IASI) and the LOTOS-EUROS atmospheric transport
model are used. The original modelled dry NH3 deposition flux from
LOTOS-EUROS and the flux inferred from IASI are compared to indicate areas
with large discrepancies between the two. In these areas, potential model or
emission improvements are needed. The largest differences in derived dry
deposition fluxes occur in large parts of central Europe, where the
satellite-observed NH3 concentrations are higher than the modelled
ones, and in Switzerland, northern Italy (Po Valley) and southern Turkey,
where the modelled NH3 concentrations are higher than the
satellite-observed ones. A sensitivity analysis of eight model input parameters
important for NH3 dry deposition modelling showed that the
IASI-derived dry NH3 deposition fluxes may vary from ∼ 20 %
up to ∼50 % throughout Europe. Variations in the NH3 dry
deposition velocity led to the largest deviations in the IASI-derived dry
NH3 deposition flux and should be focused on in the future. A
comparison of NH3 surface concentrations with in situ measurements of
several established networks – the European
Monitoring and Evaluation Programme (EMEP), Meetnet Ammoniak in
Natuurgebieden (MAN) and Landelijk Meetnet Luchtkwaliteit (LML) – showed no significant or
consistent improvement in the IASI-derived NH3 surface concentrations
compared to the originally modelled NH3 surface concentrations from
LOTOS-EUROS. It is concluded that the IASI-derived NH3 deposition
fluxes do not show strong improvements compared to modelled NH3
deposition fluxes and there is a future need for better, more robust, methods
to derive NH3 dry deposition fluxes.

Reactive nitrogen (Nr) emissions have substantially increased during
the last century to around 4 times the pre-industrial levels (Erisman
et al., 2008; Fowler et al., 2013). As a result, atmospheric deposition of
reactive nitrogen to both terrestrial and aquatic ecosystems has also
increased (Dentener et al., 2006b). Excessive nitrogen deposition to
sensitive ecosystems can cause harming effects such as soil acidification,
reduction in plant biodiversity and eutrophication in water bodies
(Erisman et al., 2015). One molecule of reactive nitrogen
may even contribute to a number of these environmental impacts through
different pathways and chemical transportation in the biosphere, the
so-called nitrogen cascade (Galloway et al., 2003). Ammonia (NH3) is
one form of reactive nitrogen and constitutes an important part of the total
amount of Nr emissions. Up to 50 % of global reactive nitrogen
emissions consist of NH3 (Reis et al., 2009), and therefore
NH3 contributes significantly to these adverse effects. Atmospheric
ammonia is deposited to surfaces by two processes: dry and wet deposition.

Dry deposition may comprise a large part of the total deposition. Earlier
modelling studies showed that dry deposition of NHx even constitutes
to over 60 % of the total deposition (Dentener et al., 2006a). The
modelled fraction of dry deposition, however, ranges hugely depending on the
used model. Deposition models in general are known to involve large
uncertainties regarding the chemistry behind NH4 formation and the
NH3 dry deposition velocities (Dentener et al., 2006a). At the same
time, large-scale assessment of NH3 dry deposition is hindered by the
extremely limited number of dry deposition observations and their sparse
distribution in space and time. Measurements of NH3 dry deposition
fluxes largely remain experimental and are limited to a few research sites
and measurement campaigns of short durations (e.g. Zoll et al.,
2016; Spindler et al., 2001). These measurements typically are
representative of a confined area and a specific ecosystem. Dry deposition
has so far been estimated on a regional scale through mainly two methods:
geostatistical approaches and atmospheric chemistry models. Geostatistical
approaches include geospatial interpolation of, or generating statistical
models based on, existing in situ observations (e.g. Erisman and
Draaijers, 1995). Atmospheric chemistry models use known and modelled
inputs (i.a. emissions) to derive dry deposition fluxes (e.g. Dentener
et al., 2006a; Wichink Kruit et al., 2012; Van der Swaluw et al., 2017). Both
methods depend strongly on the quality and availability of reliable input
information, which is often limited or even absent.

Recent developments in the use of atmospheric remote sensing to measure
NH3 distributions with large spatial coverage and daily resolution
(Van Damme et al., 2014a) allow us to examine their development in space
and time in more detail. Information from satellites can be of help to
strengthen our understanding of the complex chain of processes of
atmospheric deposition, emissions, dispersion and chemistry, especially when
complemented with information from atmospheric chemistry models. Atmospheric
chemistry models may, for example, help to fill in missing information on
NH3 concentrations close to the Earth's surface, arising from low
sensitivities of NH3 measuring instruments, or may, for instance,
supplement satellite data with information on diurnal cycles.
Nowlan et al. (2014) estimated surface concentrations and dry deposition of NO2
and SO2 by combining satellite observations of the Ozone Monitoring
Instrument (OMI) and the GEOS-Chem model. The resulting estimates compared
reasonably well with in situ measurements, thus providing a relatively
simple, data-driven method to estimate surface concentrations and dry
deposition fluxes on a worldwide scale. More recently,
Kharol et al. (2017) derived NH3 dry deposition
fluxes over North America using a similar method with NH3 observations
of the Cross-track Infrared Sounder (CrIS) satellite and the GEM-MACH model.
The aim of this paper is to search for the applicability and the limitations
of this method for NH3 over Europe using space-born observations of the
Infrared Atmospheric Sounding Interferometer (IASI) and the LOTOS-EUROS
atmospheric transport model. This paper shows the first use of the
IASI-NH3 product for the derivation of NH3 dry deposition fluxes,
together with validation of the derived NH3 surface concentrations with
in situ measurements. The latter serve as a direct proxy for the validity of
the derived NH3 dry deposition fluxes. Also, this paper is the first to
estimate the effect of modelling errors on the satellite-derived NH3
dry deposition fluxes by performing a model sensitivity study.

We start this paper with a description of the used models and datasets and
their associated uncertainties. This is followed by a description of the
methodology that is used to determine the NH3 surface concentrations
and dry deposition fluxes. Here, we also describe the design of the
sensitivity study of the LOTOS-EUROS model. The resulting estimates of the
NH3 surface concentrations and dry deposition fluxes are given. The
NH3 surface concentrations are compared to in situ measurements from
the European Monitoring and Evaluation Programme (EMEP) network in Europe. In a special case study for the Netherlands,
they are compared to in situ measurements from the Meetnet Ammoniak in
Natuurgebieden (MAN) and Landelijk Meetnet Luchtkwaliteit (LML) networks.
Moreover, a sensitivity study of the LOTOS-EUROS model is evaluated to
estimate the effect of model input uncertainties on the results that are
obtained in the same section. The study is then concluded with a discussion.

2.1 IASI NH3 product

The Infrared Atmospheric Sounding Interferometer (IASI) is a passive
remote-sensing instrument that measures infrared radiation emitted by the
Earth's surface and atmosphere within the spectral range of 645–2769 cm−1
(Clerbaux et al., 2009). The IASI-A instrument is aboard
the MetOp-A satellite that was launched in 2006 and circles in a polar
Sun-synchronous orbit. In this study, we used NH3 total column
measurements from the morning overpass, as these are more sensitive to
NH3 than the nighttime observations (Van Damme et al., 2015). The
morning overpass passes over Europe once a
day in the morning around 09:30 LT.
The NH3 product has an elliptical spatial footprint of approximately 12
by 12 km and a detection limit of 2.5 ppbv (Van Damme et al.,
2015). The retrieval uses a neural network to derive NH3 columns based
on the calculation of the hyperspectral range index (HRI), e.g. the spectral
index (Van Damme et al., 2017). The retrieval algorithm combines
information on the temperature, humidity and pressure profiles to represent
the atmospheric state closely (Whitburn et al., 2016). The retrieval uses
a fixed profile in time, based on the profiles described by Van Damme
et al. (2015). The IASI-NN (neural network) retrievals have been validated in Dammers et al. (2016)
and Dammers et al. (2017b). In these papers, they compared the
IASI-NN and Fourier-transform infrared spectrometer (FTIR) total columns and showed that the two compare reasonably
well with a systematic underestimation by the IASI-NN product of around
30 %. In this paper, the NH3 total columns observed during the warmer
season (April to September) of 2013 and 2014 are used. The warm season was
chosen because considerably fewer observations are available during the cold
months. Moreover, the observations in the cold months generally have a
higher relative uncertainty (Van Damme, 2014a). A filter has been applied
after (Van Damme et al., 2014b). This filter leaves out observations with
a relative error of <100 % unless the absolute error is smaller
than 5×1015 molecules cm−2. Figure 1 shows the mean IASI
NH3 total column concentration over Europe and the Netherlands.

Figure 1The annual mean NH3 total column concentration in 2013–2014
as observed by IASI-A in Europe (regridded to 0.50∘ longitude by
0.25∘ latitude) and the Netherlands (regridded to 0.125∘
longitude by 0.0625∘ latitude).

2.2 IASI NH3 uncertainties

The retrieval algorithm (Whitburn et al., 2016) allows estimation of
quantitative errors of each observation. The error estimate depends on a
combination of the thermal contrast (the temperature difference between
Earth's surface and atmosphere at 1.5 km) and the HRI, i.e. the spectral
footprint. The estimate also includes error terms for the uncertainty in the
profile shape and error terms arising from the used temperature and water
vapour profiles. The uncertainty estimate for each retrieved NH3 total
column is an error propagation of the individual parameter uncertainties.
Whitburn et al. (2016) showed in an error characterization that
individual retrieved NH3 columns hold the smallest errors
(∼25 %) in the situation of a high NH3 concentration
combined with a high thermal contrast. The error increases progressively
when either of these lowers. In the case of a low NH3 concentration and
a low thermal contrast, the errors can be as high as ∼270 %. More information on how the IASI-NN retrieval works and how the
relative errors are derived can be found in Whitburn et al. (2016).
Figure 2 shows the relative uncertainty of the IASI-A NH3 total column
concentrations in 2013–2014 over Europe and the Netherlands. The relative
uncertainty ranges from ∼90 % in remote areas with little
emissions to ∼30 % in high emissions areas.

Figure 2The relative error of the annual IASI-A retrieved NH3 total
column concentrations in Europe and the Netherlands in 2013–2014.

2.3 NH3 ground measurements

Ground measurements of NH3 surface concentrations from three air
quality networks were used to validate the LOTOS-EUROS and IASI-derived
NH3 surface concentrations on a monthly and a yearly basis. To do this,
observations of ambient NH3 concentrations of the EMEP network are used for Europe (EMEP,
2016). For the case study of the Netherlands, observations from two
established networks are used, the LML (RIVM, Netherlands National Institute for Public Health and the Environment) and MAN (Lolkema et al., 2015).

NH3 is challenging to measure reliably because of potential adsorption
to parts of the measurement device, leading to slow response times
(von Bobrutzki et al., 2010). The
uncertainties of the measurements may differ significantly per instruments
design. Table 1 gives an overview of the instruments used by each of these
networks and their uncertainties.

2.3.1 EMEP network

The main measurement network for reactive nitrogen concentrations on a
European scale is the EMEP network (Tørseth et al., 2012). NH3 measurements
from 35 stations were available to validate the results of 2013 and 46
stations for the results of 2014. Different types of measurement devices are
used to measure NH3 within the EMEP network. The majority of the EMEP
sites use filter packs, of which the results are relatively uncertain. In a
field intercomparison of different NH3 measurement techniques,
von Bobrutzki et al. (2010) found
that different instruments have an overall bias varying from −31.1 % to
+10.9 % for the entire data range (∼ 2 weeks),
demonstrating that there is a need for a standardized approach. For smaller
concentrations (<10 ppbv) the bias is even larger, from −22.0 %
to +54.5 %.

2.3.2 LML network

The LML has monitored hourly NH3 concentrations in the Netherlands since
1993 (van Zanten et al., 2017). Since 2014, only six
stations have been left in operation; before that, there were eight stations. The
locations of the monitoring stations were carefully selected to cover
regions with high, moderate and low emission densities equally. The
measurements are performed with AMOR instruments, which are continuous-flow
denuders. Airflow passes through a wetted rotating denuder tube in the AMOR
instrument and the NH3 absorbs into this fluid. The electric
conductivity is then determined and used as a measure for the NH3
concentration (van Zanten et al., 2017). The measurements
have a reported uncertainty of at least 9 % for hourly concentrations and
at least 7 % for yearly averages (van Zanten et al., 2017; Blank, 2001).

2.3.3 MAN network

The MAN network has provided monthly mean ambient NH3 concentrations in
nature areas in the Netherlands since 2005. The network has 236 sampling
points as of 2014, spread over 60 different nature areas. The measurements
are performed with low-cost passive samplers from Gradko. The measurements
are calibrated against the measurements of the LML
(Lolkema et al., 2015). The bottom of the passive sampler
is an open cap with a porous filter through which NH3 in the air can
enter. In the top end of the tube, the NH3 is adsorbed by an acid to
form NH4+. The NH4+ concentrations in the samplers are
analysed in a laboratory every month to compute the monthly mean NH3
concentrations. The uncertainty of the MAN measurements depends on the
NH3 concentration and varies between 20 % for high concentrations
(10–20 µg m−3) and 41 % for low concentrations
(1 µg m−3) (Lolkema et al., 2015).

2.4 The LOTOS-EUROS model

2.4.1 Model description

LOTOS-EUROS is an Eulerian chemistry transport model (CTM) (Manders et al., 2017)
that simulates air pollution in the lower troposphere. A horizontal
resolution of 0.50∘ longitude by 0.25∘ latitude,
corresponding to approximately 28 by 28 km2, is used to perform
simulations for Europe (35–70∘ N, 15∘ W–35∘ E). Secondly, for the case study of the Netherlands, the
horizontal resolution is set to 0.125∘ longitude by
0.0625∘ latitude, approximately 7 by 7 km (50.5–54∘ N,
3–7.5∘ E). The vertical resolution
of the model is a four-layer vertical grid that extends up to 3.5 km above
sea level. The bottom layer is the surface layer and has a fixed height of
25 m. On top of this layer, there is a mixing layer, followed by two
equally thick dynamic reservoir layers with time-varying thicknesses. The
model follows the mixed layer approach. LOTOS-EUROS performs hourly
calculations using meteorology provided by ECMWF (ECMWF, 2016).
Gas-phase chemistry is described using the Netherlands Organisation for
Applied Scientific Research (TNO) CBM-IV (carbon bond mechanism) scheme
(Schaap et al., 2009), which is an updated version of
the original scheme by (Whitten et al., 1980). Anthropogenic
emissions used in LOTOS-EUROS are taken from the TNO Monitoring Atmospheric Composition and Climate (MACC) III emission
database (Kuenen et al., 2014). LOTOS-EUROS uses a set of
temporal factors (monthly, daily and hourly) to break down annual total
emissions into hourly emissions. The time profile of a particular pollutant
is an aggregation of the time-dependent emission strengths from different
Selected Nomenclature for Sources of Air Pollution (SNAP) sources. The
monthly NH3 emissions peak in March and then decrease, followed by
another smaller peak in September. The daily NH3 emission strengths
are redistributed more or less evenly over the week. The hourly NH3
emission peak is reached at 13:00 LT (Denier van der Gon et al., 2011).

2.4.2 Dry deposition parameterization

The dry deposition fluxes in LOTOS-EUROS are calculated with the Deposition of Acidifying Compounds (DEPAC) 3.11
module, following the resistance
approach (van Zanten et al., 2010). In this approach, the
deposition velocity is the reciprocal sum of the aerodynamic resistance, the
quasi-laminar layer resistance and the surface resistance. A canopy
compensation point for simulation of the bi-directional flux of NH3 is
included in the implementation of the DEPAC3.11 module, following the
approach presented in Wichink Kruit et al. (2012). The
compensation point is computed dynamically using modelling results from the
last month. The model uses the CORINE/Smiatek land use map converted to the
DEPAC land use classes to determine the exchange velocities for different
land use classes. More information on the LOTOS-EUROS model can be found in
Manders et al. (2017).

2.4.3 Model performance

The LOTOS-EUROS model has participated in multiple model intercomparison
studies (e.g. Colette et al., 2017; Wichink Kruit, 2013; Bessagnet et al.,
2016; Vivanco et al., 2018), showing an overall good model performance.
LOTOS-EUROS also showed a good correspondence with yearly NH3
concentrations with a slight underestimation in agricultural areas and
overestimation in nature areas in the Netherlands (Wichink Kruit,
2013). The inferential method that we use here heavily relies on results from
LOTOS-EUROS. The model therefore has to closely represent reality, if we
wish to obtain reasonable results. As in any model, there are, however,
uncertainties associated with every part of the total chain of modelled
processes. The uncertainties related to emissions and to dry and wet
deposition are expected to impact the results the most and are discussed below.

2.4.4 Uncertainties related to emission input

Emissions are the most important input for any CTM and are, at the same
time, a source of substantial uncertainties (Reis et al., 2009; Behera et
al., 2013). NH3 emissions are relatively uncertain due to the diverse
nature of agricultural sources leading to large spatial and temporal
variations in emissions. The uncertainty of the European reported annual
totals is estimated to be around ±30 % (EMEP, 2016). The
uncertainty is larger for countries that have limited research on their
emission inventory and carry out a few emission measurement activities.

The presence of other gaseous components such as SO2 and NOx may
have a high impact on the modelled NH3 concentrations, as NH3 in
the atmosphere reacts readily with sulfuric acid (H2SO4) and
nitric acid (HNO3) to form particulate ammonium (e.g.
(NH4)2SO4 or NH4NO3). It is therefore also
important to consider the errors in the SO2 and NOx emissions.
The SO2 emissions are relatively well known per source category and
thus hold a relatively low uncertainty of about ±10 % on reported
annual totals. The uncertainty in the NOx emissions is higher, of
around ±20 % on reported annual totals. However, due to
interpolation to account for missing data for some countries, the final
uncertainty of the annual totals of both SO2 and NOx is estimated
to be higher (Kuenen et al., 2014).

Needless to say, one single emission at a certain time may have a much
higher error due to the large uncertainty related to redistribution and the
timing of emissions (Hendriks et al., 2016; Skjøth et al., 2011). More
information on the quality data ratings of NH3, SO2 and NOx
per source category and per country can be found in the report of the
European Environment Agency (EEA, 2016).

2.4.5 Uncertainties regarding dry and wet deposition

The second source of uncertainties originates from the model
parameterization of both dry and wet deposition. Several multi-model studies
(e.g. Dentener et al., 2006a; Colette et al., 2017; Wichink Kruit,
2013; Flechard et al., 2011; Vivanco et al., 2018) have shown that there is
quite a large discrepancy in the implementation of dry and wet deposition in
different CTMs. A fundamental input for estimating dry deposition fluxes in
CTMs is the uncertainty in the deposition velocity. Schrader and
Brummer (2014) compiled a database of the NH3 deposition velocities per
land use category that have been used in several deposition models from 2004
to 2013. The results showed quite a large variation in the Vd values
for different land use classes. Some classes (e.g. water, urban) showed only
a small variation in Vd of an interquartile range of ∼5
to 10 % for 50 % of the data. Other classes (e.g. coniferous,
agriculture) showed a much larger interquartile range in Vd of
∼30 to 40 %. Flechard et al. (2011) compared four
existing dry deposition routines across 55 Nr monitoring sites and
found that the differences between models reach a factor 2–3 and are often
larger than differences between monitoring sites. Erisman (1993)
estimated the dry and wet deposition fluxes of acidifying substances in the
Netherlands from measured and modelled concentrations. The estimated
uncertainty in the average NH3 fluxes in this paper was estimated to be
30 %, with a systematic error of 30 % in the used Vd for NH3.
Dentener et al. (2006a) calculated the deposition of Nr with 23
atmospheric chemistry transport models in a multi-model evaluation. Although
there were quite large differences between the different models, the paper
showed that 71.7 % of the model-calculated mean wet deposition rates in
Europe agreed to within ±50 % with NH4+ wet deposition
measurements from the EMEP network.

The NH3 surface concentrations and dry deposition fluxes are estimated
by combining the observations of the IASI-A satellite instrument and the
modelling results from LOTOS-EUROS. The method is an adapted version of the
approach for NO2 and SO2 presented by Nowlan et al. (2014). The
IASI-A instrument only observes the NH3 total column at overpass time.
We use the modelling results of LOTOS-EUROS to account for the diurnal
variation in the atmospheric concentrations of NH3. The vertical
NH3 profiles in LOTOS-EUROS are also used to deduce the ground-level
NH3 concentrations from IASI. The computation of the IASI-derived
NH3 surface concentrations and dry deposition fluxes is described in
detail in the following sections.

3.1 Surface concentration computation

The NH3 total column observations from IASI are first regridded onto
the LOTOS-EUROS model grid. The monthly mean NH3 total column
concentrations are then calculated for each pixel. We use the vertical
profile of NH3 per grid cell in LOTOS-EUROS to relate the IASI NH3
total column to NH3 surface concentrations. The IASI-derived NH3
surface concentrations (CIASI) are computed
following Eq. (1):

(1)CIASI=ΩIASIΩoverpassLE⋅CLE.

Here, ΩIASI represents the monthly mean NH3
total column concentration from IASI (molecules cm−2),
ΩoverpassLE represents the modelled NH3 total
column at overpass time in LOTOS-EUROS (molecules cm−2), and
CLE is the modelled mean surface concentration
(µg m−3), the concentration in the lowermost layer in LOTOS-EUROS.

3.2 Dry deposition flux computation

The hourly NH3 dry deposition fluxes are modelled in LOTOS-EUROS. The
modelled NH3 dry deposition fluxes are then adjusted based on actual
observations from IASI. The modelled and the IASI-derived NH3
concentrations share the same vertical profile. The ratio of the observed
and the modelled total column concentrations, rather than the surface
concentrations, is therefore directly used to alter the modelled NH3
dry deposition flux. The NH3 dry deposition flux (kg N ha−1 yr−1)
inferred from IASI, FIASI, is
computed following Eq. (2):

(2)FIASI=ΩIASIΩ0verpassLE⋅FdailyLE.

Here, ΩIASI denotes the NH3 total column
concentration from IASI, Ω0verpassLE the modelled NH3 total column at
overpass time in LOTOS-EUROS (molecules cm−2) and
FdailyLE the total daily NH3 dry
deposition flux in LOTOS-EUROS (kg N ha−1yr−1).
FdailyLE is the sum of the hourly
NH3 dry deposition fluxes, as shown in Eq. (3):

(3)FdailyLE=∑h=124FhLE=∑h=124VdChLE-χtot,hLE.

The hourly NH3 dry deposition flux is the product of the dry deposition
velocity Vd and the difference between the hourly NH3 surface
concentration, ChLE and the total
compensation point of NH3, χtot,hLE. To account for the high variability of
atmospheric NH3 and the limiting amount of available IASI observations,
monthly means of these values are used rather than daily values.

3.3 Sensitivity analysis

The main sources of model uncertainties that are relevant for deposition
modelling arise from uncertainties in the emission input and the deposition
parameterizations (see Sect. 2.3).

A total of four input fields were varied in LOTOS-EUROS: the MACC-III NH3
emissions, the MACC-III NOx and SO2 emissions, the dry deposition
velocity, Vd, of NH3 and the wet deposition of NH3. The wet
deposition is varied by adjustment of the gas scavenging constant,
Gscav, for NH3. The wet scavenging constant Gscav
linearly influences the amount of NH3 wet deposition. This results in
changes in the wet NH3 deposition flux of +30 % and −30 %, too.
The objective of these eight sensitivity runs is to assess the uncertainty
ranges on the estimated dry NH3 deposition fluxes resulting from
modelling errors. Table 2 gives an overview of the parameters that are
varied. We chose to apply a constant perturbation of +30 % and −30 %
to one field at the time to see their individual effect and to improve the
comparability of the results, too. Moreover, perturbations of ±30 %
are reasonable ranges since they correspond to the estimated uncertainties
in the MACC-III emission fields' annual totals and the uncertainties in the
wet and dry deposition fluxes of NH3.

Table 2Perturbations on input fields that have been used for the
sensitivity analysis of the method.

4.1 NH3 surface concentrations

4.1.1 Europe

Figure 3 shows the warm season (April–September) mean NH3 surface
concentrations in 2013 and 2014. Figure 3a, c, e, g show the modelled
concentrations from LOTOS-EUROS (which we will refer to as the “modelled
concentrations”) and concentrations that are derived from IASI in
combination with LOTOS-EUROS (which we will refer to as “IASI-derived
concentrations”). The dots represent the corresponding measurements from the
EMEP stations. Figure 3b, d, f, h show the absolute differences between the
EMEP measurements and the modelled and IASI-derived concentrations. In
general, the pattern of the EMEP measurements and the modelled and
IASI-derived concentrations matches quite well. The majority of the EMEP
measurements agree with the modelled and IASI-derived concentrations to
−0.75 to +0.75µg m−3. The sum of the absolute differences
between the warm season mean NH3 surface concentrations in a cubic
metre from EMEP and LOTOS-EUROS was 23.0 µg in 2013 and 32.5 µg in
2014. The sum of the absolute differences between the warm season mean
NH3 surface concentrations from EMEP and IASI was slightly lower:
22.6 µg in 2013 and 28.0 µg in 2014.

Figure 3Comparison of the warm season (April–September) mean NH3
surface concentrations (µg m−3) from LOTOS-EUROS and derived from
IASI and the warm season mean NH3 surface concentrations measured by
the EMEP stations in 2013 (a, b, c, d) and 2014 (e, f, g, h). The absolute
differences between the two are shown in the right figures.

Figure 4 shows scatterplots of the monthly mean (Fig. 4a, b, e, f) and the warm season
mean (Fig. 4c, d, g, h) NH3 surface concentrations. The x axis represents
concentrations measured by the EMEP stations. The y axis represents either
the modelled concentrations (blue) or the IASI-derived concentrations
(orange). The monthly mean modelled concentrations and the EMEP measurements
show a reasonably strong linear relationship in 2013 (r=0.71). The
correlation between the two was weaker (r=0.39) in 2014. The correlation
between the IASI-derived concentrations and the EMEP measurements was
similar in 2013 (r=0.71) and was higher in 2014 (r=0.46). The warm
season mean IASI-derived concentrations and the EMEP measurements have a
slightly stronger correlation coefficient and an improved slope compared to
the modelled concentrations.

Figure 5Mean of the NH3 surface concentrations at all EMEP locations
per month (green line) and the coinciding NH3 surface concentrations
from LOTOS-EUROS (blue line) and derived from IASI (orange line) in 2013
(a) and 2014 (b). The absolute differences between EMEP and LOTOS-EUROS are
shown in blue and the absolute differences between EMEP and IASI are shown
in orange.

Figure 5 shows the mean NH3 surface concentration of all EMEP stations
per month and the corresponding modelled and IASI-derived concentrations at
the same locations. The absolute differences per month are plotted in the
same figure in blue (LOTOS-EUROS vs. EMEP) and orange (IASI-derived vs. EMEP).
All concentration time profiles show a peak value in April, resulting from
spring fertilization. The LOTOS-EUROS time profile at the EMEP locations
decreases from April to May and starts to increase towards the end of the
year. The time profile of the EMEP stations follows the same pattern from
April to June but decreases towards the end of the year. The IASI-derived
time profile shows a decreasing pattern, except in August, where there is a
small peak. The IASI-derived time profile shows a relatively better
comparison with the EMEP measurements in April and July to September in 2013
and in April and September in 2014. The sum of the absolute differences of
the mean NH3 surface concentrations in a cubic metre at all EMEP
locations between LOTOS-EUROS and EMEP amounts to 3.1 µg in 2013 and
2.5 µg in 2014. The sum of the absolute differences between IASI and
EMEP was somewhat smaller in 2013, amounting to 1.7 µg, and somewhat
higher in 2014, amounting to 3.0 µg.

In summary, the majority of the IASI-derived concentrations showed a
slightly stronger correlation with the EMEP measurements than modelled
concentrations on a monthly basis. The correlation became more pronounced on
a seasonal basis (mean of April–September).

4.1.2 The Netherlands

Comparison with LML measurements

Figure 6 shows the warm season (April–September) mean NH3 surface
concentrations (µg m−3) in the Netherlands in 2013 and 2014. The
corresponding LML measurements are plotted on top of the modelled and
IASI-derived concentrations. LOTOS-EUROS seems to capture the general
pattern of the LML measurements fairly well in both 2013 and 2014. The sum
of the absolute differences between the warm season mean NH3 surface
concentrations in a cubic metre from LML and LOTOS-EUROS was 47.3 µg in
2013 and 44.8 µg in 2014. The sum of the absolute differences between
the warm season mean NH3 surface concentrations from LML and IASI was
slightly lower in 2013, namely 44.9 µg, and somewhat higher in 2014,
namely 48.5 µg.

Figure 6Comparison of the warm season (April–September) mean NH3
surface concentration in 2013 (a, b, c, d) and in 2014 (e, f, g, h) from
LOTOS-EUROS and derived using IASI. The corresponding warm season mean
NH3 surface concentrations measured by the LML stations are plotted
on top of the left figures. The right figures depict the differences between
the two.

Figure 7 shows scatterplots of the monthly mean NH3 surface
concentrations (µg m−3). The x axis depicts the LML measured
concentrations. The y axis depicts the corresponding modelled and
IASI-derived concentrations. The modelled concentrations and the LML
measurements show a moderate linear relationship (r=0.39 in 2013, r=0.50 in 2014). The high NH3 concentration stations (Vredepeel and
Wekerom) are underestimated by LOTOS-EUROS. The other stations are closer to
the 1:1 line and appear to match quite well. The correlation
coefficient of the IASI-derived concentrations and the LML measurements is
r=0.39 in 2013 and r=0.53 in 2014. The IASI-derived concentrations also
underestimate the high-concentration LML stations (Vredepeel and Wekerom) in
both years. The majority of the low-concentration LML stations are
overestimated by the IASI-derived concentrations in 2013 and underestimated
by the IASI-derived concentrations in 2014. In general, both high and low
LML measurements were reproduced inadequately by the IASI-derived
concentrations. The elimination of the high-concentration stations (Vredepeel
and Wekerom) does not lead to a better comparison of the LML measurements to
the IASI-derived concentrations.

Figure 7Comparison of the monthly mean NH3 surface concentrations
measured by the LML stations and the corresponding LOTOS-EUROS and
IASI-derived NH3 surface concentrations during the warm season
(April–September) of 2013 (top) and 2014 (bottom). The high-concentration
stations (Vredepeel and Wekerom) are eliminated from the right figures (c, d, g, h).

Table 3 gives a month-by-month comparison of the correlation coefficient,
the slope and the intercept of the monthly mean NH3 surface
concentrations of all LML stations vs. the corresponding modelled and
IASI-derived concentrations. In 5 out of 12 months, the IASI-derived
concentrations and the LML measurements have a better correlation
coefficient and slope compared to the modelled concentrations and the
LML measurements. The modelled concentrations are consistently lower than
the LML measurements.

In short, the IASI-derived concentrations do not show a better comparability
with the LML measurements compared to the modelled concentrations.

Table 3Month-by-month comparison of the correlation coefficient (r), slope
and intercept of the monthly mean NH3 surface concentrations of the LML
stations (x axis) and the coinciding monthly mean LOTOS-EUROS and
IASI-derived NH3 surface concentrations (y axis). The arrows
denote which of the two (LOTOS-EUROS or IASI) gives the most desirable
value. The arrows are attributed to either LOTOS-EUROS or IASI based
on the following criteria: highest r, slope closest to 1, intercept closest
to 0 and smallest RMSD.

Comparison with MAN measurements

Figure 8 shows the warm season mean NH3 surface concentrations in the
Netherlands in 2013 and 2014. The dots represent the corresponding MAN
measurements. The patterns of the MAN measurements are captured quite well
by the modelled concentrations, with low NH3 surface concentrations
near the coast and increasing values towards the east of the Netherlands.
The sum of the absolute differences between the warm season mean NH3
surface concentrations in a cubic metre from MAN and LOTOS-EUROS was
444.7 µg in 2013 and 494.3 µg in 2014. The sum of the absolute
differences between the warm season mean NH3 surface concentrations
from MAN and IASI was slightly higher in both years, amounting to
512.1 µg in 2013 and 513.6 µg in 2014.

Figure 8Comparison of the warm season (April–September) mean NH3
surface concentration in 2013 (a, b, c, d) and in 2014 (e, f, g, h) from
LOTOS-EUROS and derived using IASI. The corresponding warm season mean
NH3 surface concentrations measured by the MAN stations are plotted
on top of the left figures. The right figures depict the differences between
the two.

Figure 10Mean of the NH3 surface concentrations at all MAN locations
per month (green line) and the coinciding NH3 surface concentrations
from LOTOS-EUROS (blue line) and IASI (orange line) in 2013 (a) and 2014
(b). The absolute differences between MAN and LOTOS-EUROS are shown in blue
and the absolute differences between MAN and IASI are shown in orange.

Figure 9 shows scatterplots of the monthly mean (Fig. 9a, b, e, f) and warm season mean
(Fig. 9c, d, g, h) NH3 surface concentrations. The x axis depicts the MAN
measurements. The y axis depicts the corresponding modelled or IASI-derived
concentrations. The modelled concentrations and the MAN measurements show a
moderate positive linear relationship (r=0.5 in 2013, r=0.46 in
2014). The correlation of the IASI-derived concentrations and the MAN
measurements is somewhat weaker in both years (r=0.40 in 2013, r=0.38 in 2014). The IASI-derived concentrations and the MAN measurements show
a similar to slightly stronger correlation (r=0.59 in 2013, r=0.54
in 2014) compared to the modelled concentrations and the MAN measurements
for the warm season (r=0.54 in 2013, r=0.54 in 2014).

Figure 10 shows the mean NH3 surface concentration of all MAN stations
per month and the corresponding modelled and IASI-derived concentrations at
the same locations. The absolute differences per month are plotted in blue
(LOTOS-EUROS vs. MAN) and orange (IASI-derived vs. MAN). The mean of all MAN
stations peaks in April in both years. In 2013, the mean of all MAN stations
increases from May on, peaks in July and then decreases towards the end
of the year. In 2014, there is an additional peak in July, followed by
another decrease.

The sum of the absolute differences of the mean NH3 surface
concentrations in a cubic metre at all MAN locations between LOTOS-EUROS and
MAN amounts to 7.2 µg in 2013 and 10.9 µg in 2014. The sum of the
absolute differences between IASI and MAN was somewhat larger in 2013,
amounting to 7.9 µg, but considerably smaller in 2014, amounting to 6.0 µg.

Table 4Month-by-month comparison of the correlation coefficient (r), slope
and intercept of the monthly mean NH3 surface concentrations of the MAN
stations (x axis) and the coinciding monthly mean LOTOS-EUROS and
IASI-derived NH3 surface concentrations (y axis). The arrows
denote which of the two (LOTOS-EUROS or IASI) gives the most desirable
values. The arrows are attributed to either LOTOS-EUROS or IASI based
on the following criteria: highest r, slope closest to 1, intercept closest
to 0 and smallest RMSD.

Figure 11The absolute differences between the monthly mean NH3
surface concentrations modelled in LOTOS-EUROS (blue) and derived from IASI
(orange) and the monthly mean NH3 surface concentrations measured by
the MAN stations in the warm season (April–September) in 2013 (a) and 2014
(b), grouped as function of the MAN monthly mean NH3 surface
concentrations. The black line indicates the median, the edges of the boxes
indicate the 25th and the 75th percentiles (Q1 and Q2), the
whiskers indicate the full range of the absolute differences (Q1 − 1.5*IQR
and Q3 + 1.5*IQR), and the dots indicate the outliers values that lie
outside the whiskers.

Table 4 shows the correlation coefficient, the slope and the intercept of
the MAN measurements vs. the modelled and IASI-derived concentrations for
the warm months in 2013 and 2014. In 2013, the IASI-derived concentrations
show a weaker correlation with the MAN measurements than the modelled
concentrations in all months. Only in May and June in 2014, the IASI-derived
concentrations compared slightly better to the MAN measurements than the
modelled concentrations.

The data are grouped into different MAN NH3 surface concentration ranges
to test the performance of the modelled and IASI-derived concentrations as a
function of concentration level. Figure 11 shows the grouped absolute
differences between the monthly mean NH3 surface concentrations
measured by the MAN stations and the corresponding modelled (blue) and
IASI-derived (orange) concentrations. For low MAN concentration ranges
(0–10 µg m−3), the modelled concentrations agree fairly well with the MAN
measurements in both years. For higher MAN concentration ranges
(>10µg m−3), the model seems to underestimate the
monthly mean NH3 surface concentrations. The IASI-derived
concentrations were relatively higher than the modelled concentrations for
all concentration levels in 2013. The opposite is true in 2014, where the
IASI-derived concentrations were relatively lower than the modelled
concentrations. We conclude that the differences between modelled and
IASI-derived concentrations in the Netherlands cannot be assigned to
specific concentration levels.

In summary, the comparison with the MAN measurements does also not show any
significant or consistent improvement in the IASI-derived concentrations
compared to the modelled concentrations.

4.1.3 Summary of the comparison with in situ measurements

We compared the modelled and IASI-derived concentrations to measurements of
the European EMEP network. The IASI-derived concentrations showed in general
a slightly stronger correlation with the EMEP measurements than modelled
concentrations on a monthly basis. Moreover, the correlation became more
pronounced on a seasonal basis (mean of April–September). We then compared
the modelled and the IASI-derived concentrations to measurements of Dutch
MAN and LML networks. This comparison, on the other hand, did not show any
significant or consistent improvement in the IASI-derived concentrations
compared to the modelled concentrations.

In general, both the modelled and the IASI-derived concentrations seem to be
overestimated in emission areas. This could potentially be related to the
overpass time of the satellite. In high emission areas, the NH3
concentrations are more variable in time, and the IASI observations might
have an uncertain representativeness. Moreover, the measurements in high
emission areas are generally more uncertain with regard to their spatial
representativeness. Overall, these measurements can be more affected by
local rather than regional sources.

Generally, the modelled and the observed NH3 total columns match quite
well. This means that the LOTOS-EUROS model represents the spatial
distribution of NH3 rather well. There are some areas with large
discrepancies between the two where we see considerable deviations in the
modelled and the IASI-derived concentrations. Most of these areas, however,
cannot be validated against measurements, because of the lack of
measurements here. The changes in the comparison of the available
measurements with modelled vs. IASI-derived concentrations are therefore
relativity small. Based on the measurements we have, we conclude that we do
not see any significant improvement in the IASI-derived concentrations
compared to the modelled concentrations.

The differences between Europe and the Netherlands could be explained by the
location of the ground measurements. The majority of the European-scale
stations are located in background regions, with relatively well-mixed and
low NH3 concentrations. Most stations in the Netherlands, on the other
hand, are located in, or nearby, regions with relatively higher NH3
concentrations. As a result, the vertical profile shapes in LOTOS-EUROS in
the Netherlands are more complex and variable in time, as this region is
influenced by a constantly changing combination of transport, emission and
deposition. The use of an inadequate vertical profile to derive NH3
surface concentrations from IASI could lead to an erroneous redistribution
of the total amount of measured NH3, therewith worsening the
comparability with in situ measurements. On the contrary, the vertical
profile shapes in background regions are more stable and constant in time,
and therefore more likely to be described adequately by the LOTOS-EUROS model.

Side note on validation with in situ measurements

The differences between the in situ measurement and the modelled and
IASI-derived concentrations can partially be explained by their discrepancy
in terms of spatial representation, which limits their comparability to some
extent. The footprint of the in situ measurements is relatively small and
easily influenced by local factors, whereas the model and the satellite
provide us with a mean value over a much larger area. The two high-concentration stations of the LML network in the Netherlands, Vredepeel and
Wekerom, are, for instance, influenced by nearby emission sources which cannot
be resolved by regional models at the current resolution.

4.2 NH3 dry deposition flux

4.2.1 Europe

The monthly mean dry NH3 deposition flux has been computed for the warm
season (April to September) in 2013 and 2014. Figure 12 shows the warm
season mean dry NH3 deposition flux (kg N ha−1 yr−1).
Figure 12a, c show the original, modelled flux from LOTOS-EUROS (which will be
referred to as the “modelled flux”). Figure 12b, d show the modelled
flux adjusted by the IASI satellite observations (which will be referred to
as “IASI-derived flux”). The modelled fluxes were very similar in both
years. Figure 13 shows the absolute and relative differences between the
modelled and the IASI-derived flux. In 2013, the IASI-derived fluxes were
higher than the modelled fluxes in the Netherlands and Belgium. This depicts
that the IASI-observed NH3 total columns here were higher than the
modelled total columns in LOTOS-EUROS. The IASI-derived fluxes were higher
than the modelled fluxes in other areas such as Germany and large parts of
central Europe, mainly in Poland, Belarus and Romania. In 2014, the
IASI-derived fluxes were much higher than the modelled flux in parts of
central Europe, mainly in Poland and the Czech Republic, and in parts of the
United Kingdom, for instance, Northern Ireland. In both years, the IASI-derived
fluxes were much lower than modelled fluxes in Switzerland, the Po Valley in
Italy and the northern part of Turkey. Here, the IASI-observed NH3
total columns were thus consistently lower than the modelled total columns
in LOTOS-EUROS. Inadequate emission input data could explain the differences
at these locations. Another possible cause is incorrect modelling of the
atmospheric transport and/or stability of NH3 in LOTOS-EUROS.

4.2.2 The Netherlands

The modelled and IASI-derived fluxes in the Netherlands are shown in
Fig. 14. Figure 14 shows that the modelled fluxes were similar in both
years, whereas the IASI-derived flux varied quite a lot. The IASI-derived
flux was higher than the modelled flux in 2013 and lower than the modelled
flux in 2014. The IASI-observed NH3 total columns in the Netherlands
were thus in general somewhat higher than the modelled NH3 columns in
2013 and somewhat lower than the modelled NH3 columns in 2014.

Figure 16The median change (%) in the terrestrial NH3 dry
deposition flux in 2014 in (kg N ha−1 yr−1) from LOTOS-EUROS
(a) and IASI-derived fluxes (b), resulting from different perturbations of model inputs
of LOTOS-EUROS. The orange lines indicate the 25th and the 75th quartiles.

Figure 15 depicts the absolute and relative differences between the modelled
and IASI-derived fluxes. In 2013, the main differences occurred in the central
and northernmost parts of the Netherlands, where the IASI-derived fluxes were
clearly higher than the modelled ones. Furthermore, the IASI-derived fluxes
were higher than the modelled fluxes for the largest part of the Netherlands.
In 2014, the IASI-derived fluxes were lower than the modelled fluxes for the
largest part of the Netherlands, except for the centre and the northernmost part.

4.2.3 Interannual differences

The interannual variations of the modelled and IASI-derived flux
differences (see Figs. 13 and 15) could be related to different
meteorological conditions. The annual global climate reports from the National Oceanic and Atmosphere Administration
(NOAA) show that the mean
temperatures in Europe were higher in 2014 than in 2013, especially in
western Europe. This might have had an effect on the actual emissions and
their variability, which is only limited taken into account by the model.
The annual precipitation in both years was near average for Europe as a
whole. However, if we zoom in to a more regional scale, we see that it was
much wetter than average during the warm season in nearly all parts of the
Balkan peninsula and Turkey (NOAA, 2014,
2015). Figure 13 shows
that the largest interannual variations on a European scale occur around
the Black Sea: in Ukraine but also in the eastern parts of the Balkan
peninsula and Turkey. Some of these regions thus coincide with regions that
experienced heavy rainfall in 2014 and might have affected emission and
deposition processes which are not taken into account by the model. This
suggests that meteorological effects might indeed influence our results.
However, the examined period of two warm seasons only is too short to draw a
conclusion.

4.3 LOTOS-EUROS sensitivity study

The results of the sensitivity runs are summarized in Figs. 16, 17 and 18.
Figure 16 shows the relative changes in the warm season mean terrestrial dry
NH3 deposition flux over Europe modelled in LOTOS-EUROS (Fig. 16a) and derived
from IASI (Fig. 16b) in 2014 for different model runs. The mean LOTOS-EUROS dry
NH3 deposition over the land cells in the modelling grid in 2014 was
1.76 kg N ha−1 yr−1. The mean IASI-derived dry NH3 deposition
flux was somewhat higher, namely 2.20 kg N ha−2 yr−1.

Variations in the MACC-III NH3 emissions caused the largest changes in
the modelled flux. The smallest change was obtained by variation of the wet
deposition scavenging coefficient Gscav. The variations in the dry
deposition velocity Vd led to the biggest changes in the IASI-derived
flux. The effect appears to be amplified compared to the effect on the
modelled flux. The effect of the MACC-III NH3 emissions is damped. On
the other hand, the effect of the MACC-III NOx and SO2 emissions
is also amplified. The signs of the changes in the IASI-derived flux have
flipped because of the changes in MACC-III NH3, MACC-III NOx and
SO2 and Gscav. The modelled flux is 1:1 sensitive to
emission changes in NH3, whereas for IASI-derived flux this is much
lower. The IASI-derived flux, in turn, changes 1:1 with the Vd.

The variations in the modelled flux are a result of daily and monthly
variations in emissions. The variations in the IASI-derived flux are also a
result of these variations, but on top of this they also include an effect of the
overpass time of the satellite.

Figure 17 shows the changes (%) of monthly mean IASI-derived fluxes in
2014 resulting from the different LOTOS-EUROS sensitivity runs. Note that
the effect of the runs with changes in wet deposition through variations of
the gas scavenging coefficient for NH3 is enlarged by a factor of 10. We
see that the changes with respect to the standard LOTOS-EUROS run are in
general constant over the months. The least variation is observed for the
runs with changed Vdry values, that all resulted in a change of
∼31 % per month. The runs with adjusted MACC-III emissions
of NH3 and emissions of NOx and SO2 led to largest changes
in May and the smallest changes in September. The maximum differences between
months are 9.5 % and 5.6 %, respectively, for the runs with adjusted
NH3 and the runs with adjusted NOx and SO2 values. The runs
with changed values of Gscav for NH3 seem to be affected most by
changing weather conditions, which resulted in the relatively largest
variation per month. However, because the changes in the IASI-derived flux
are small (−2.4 % to +1.7 %), we now continue to look at yearly changes.

Figure 18 shows the relative standard deviation (%) of all eight sensitivity
runs for Europe. Figure 18d shows the relative standard
deviation of the final IASI-derived flux. The relative standard deviation
varies from ∼20 % to ∼50 % throughout
Europe. The smallest variations can be seen in the southwestern and central
parts of Europe. The highest variations, of ∼40 %–50 %, are
mainly found in long-distance transport areas with low NH3
concentrations and deposition fluxes, such as Scandinavia, and in areas with
high aerosol precursor emissions, such as the Balkans.

In this paper, we examined the applicability and the limitations of the
method suggested by Nowlan et al. (2014) for the derivation of NH3
surface concentrations and dry deposition fluxes across Europe. A
comparison of the LOTOS-EUROS modelled and IASI-derived NH3 surface
concentrations with in situ measurements of the EMEP network on a European
scale and the LML and MAN networks in the Netherlands has been made. Although
there appeared to be some improvements in the IASI-derived NH3
surface concentrations compared to the modelled LOTOS-EUROS NH3
surface concentrations, mainly in background regions, there did not seem
to be any significant, consistent improvement. In addition, the timing of
the IASI-derived NH3 surface concentrations did not show better
correspondence with the in situ observations than the modelled NH3
surface concentrations. Consequently, as the dry NH3 deposition
fluxes are directly derived from the NH3 surface concentrations,
no significant improvement is expected here either. On top of this, the
sensitivity study using eight input parameters important for NH3 dry
deposition modelling showed that the effect of model uncertainties on the
IASI-derived dry NH3 deposition fluxes is amplified by the estimation
procedure compared to the effect on the model simulations itself. The final
IASI-derived dry NH3 deposition fluxes can vary ∼20 % up to ∼50 % throughout Europe as a result of model
uncertainties.

The method used to derive the NH3 surface concentrations and dry
deposition fluxes from IASI observations is based on various assumptions.
For one, the method assumes that the relationship between the NH3
concentration and the dry deposition flux is linear, whereas this
relationship is in reality non-linear. In fact, these quantities can even be
anti-correlated with highest surface concentrations during the night when
the atmosphere is stable and the exchange is limited. The compensation point
of NH3 further enhances the non-linearity. For our purpose, we
focus on a single time of the day using monthly data; however,
approximating this concentration–flux relationship by a linear curve may
seem reasonable for concentration regimes below the saturation point. For
higher NH3 surface concentrations the current approach will likely lead
to overestimated dry deposition fluxes. Moreover, this study includes the
impact of the compensation point of NH3 through the dry deposition
scheme in LOTOS-EUROS. Although the uncertainties are relatively large as
the used compensation points are based on relatively few observations (e.g.
Wichink Kruit et al., 2007), we feel that the
inclusion of the compensation point is a strong point of this study.

Moreover, the approach by Nowlan also assumes that the NH3 total
column concentrations measured by IASI serve as a direct proxy of the
NH3 surface concentrations. In reality, however, the relationship
between the two is influenced by various different factors, including the
vertical distribution of NH3 and the satellite's sensitivity. There are
already quite some uncertainties involved with the vertical distribution of
NH3, and therefore tower measurement campaigns (Dammers et al.,
2017a; Li et al., 2017a) are very important to strengthen our understanding.
Dammers et al. (2017a), for instance, showed that the daytime boundary
layer is well mixed, which supports the choice of a model that uses the
mixed layer approach such as LOTOS-EUROS. Li et al. (2017b) showed that
there is a clear seasonal variation in the vertical distribution of NH3
and that the slope of the NH3 concentration gradient varies
throughout the year. During winter, Li et al. (2017b) observed relatively
high NH3 ground concentrations due to potential trapping of NH3
emissions in a shallow winter boundary layer and reduced NH3
concentrations higher up the column. In these types of situations, the
IASI satellite instrument potentially misses high NH3 ground
concentrations because of the lack of sensitivity to the lower parts of the
boundary layer. The computation of averaging kernels for IASI could help to
indicate more precisely where the sensitivity lies and how the measured
total columns are distributed. Moreover, further development and validation
of the IASI retrieval may help to improve our understanding of the
satellite's product, therewith also increasing its applicability.

The method also assumes that the timing and distribution of the emissions in
the LOTOS-EUROS model closely represent reality, as the ratio between the
retrieved and the modelled ammonia burden is used at overpass time. The
accuracy of the seasonal variation in the NH3 emissions in LOTOS-EUROS
is therefore of great importance. The reliability of yearly dry NH3
deposition estimates using our method is limited by the lack of high-quality
IASI observations during the cold season. As a result, derivation of yearly
IASI-derived NH3 dry deposition estimates may differ substantially
depending on whether or not the spring maximum peak occurs in the
satellite-observed months (April–September). Skjøth et al. (2011) presented the seasonal
variation and the distribution of NH3 emissions for different European
countries per agricultural source. They showed, for instance, that
approximately half of the NH3 emissions from spring fertilization are
usually emitted in March. As the spring fertilization amounts to
∼20 %–50 % of the yearly total NH3 emissions, this may
result in a variation of the same magnitude on the subsequent deposition
estimates. Improvement of the seasonal variation in NH3 emissions in
LOTOS-EUROS could be used to fill in this gap and lead to a more accurate
representation of reality. Skjøth et al. (2011)
showed that the implementation of a dynamic NH3 emission model for
different agricultural sources may result in considerable model performance
improvements when high-quality activity data and information on spatial
distributions of emissions are available. Furthermore, Hendriks et al. (2016)
showed that the use of manure transport data for NH3 emission
time profiles leads to additional model improvements and a better
representation of the spring maximum.

Moreover, mismatches between the actual and modelled diurnal variations in
NH3 emission could also easily lead to large differences in the
IASI-derived dry NH3 deposition estimates. As an illustration,
Sintermann et al. (2016), for instance, measured NH3 emissions
from an agricultural surface after slurry application and showed that
∼80 % of the total NH3 was emitted within 2 h.
Combined with the short lifetime of NH3, there is a possibility that the
IASI instrument completely misses these kinds of events if they occur after
its overpass. A possible way to reduce the impact of the diurnal variation
is to combine information from IASI with other satellites that have
different overpass times. NH3 observations from the CrIS satellite
instrument could, for instance, be used (Shephard and
Cady-Pereira, 2015).

At this stage, we can conclude that the IASI-derived NH3 deposition
fluxes do not show strong improvements compared to modelled NH3
deposition fluxes and there is a future need for better, more robust,
methods to derive NH3 dry deposition fluxes. This could potentially be
achieved by further integration of existing in situ and satellite data into
models with special attention to data representativeness, for instance, by
means of data assimilation. In addition, there is a need for a better
understanding of the surface exchange of NH3 for different land use
types. Model parameterizations of the surface exchange of NH3 are now
based on a limited number of direct flux measurements, and more measurements
could definitely improve this. Also, a better understanding of the timing
and distribution of NH3 emissions could lead to considerable
improvements in modelled emission fields and consequently deposition fields
from CTMs.

JWE and ED had the
initial ideas
to start this study. SCvdG performed the model simulations and
data analysis. ED contributed to the processing of the IASI
satellite data. MS provided guidance with the model simulations.
All co-authors contributed to the interpretation of the results.
SCvdG wrote the paper with contributions from all co-authors.

The authors would like to thank the “Centre National d'Etudes Spatiales”
(CNES, France) for building and developing IASI and for sharing their
products. The MetOp satellites are part of the EUMETSAT Polar System. We
acknowledge the Aeris website (https://iasi.aeris-data.fr/nh3/, last access: 10 January 2018)
for providing access to the IASI-NH3 dataset. Moreover, we would like
to thank RIVM and EMEP for maintaining, collecting and sharing data
from their NH3 ground networks.

Wichink Kruit, R. J.: ECLAIRE model inter-comparison of atmospheric nitrogen
deposition and concentrations over Europe, presentation at the ACCENT-Plus
Symposium held in September in Urbino, Italy, 2013.

A combination of NH3 satellite observations from IASI and the LOTOS-EUROS model is used to derive NH3 surface concentrations and dry deposition fluxes over Europe. The results were evaluated using surface measurements (EMEP, LML, MAN) and a sensitivity study. This is a first step in further integration of surface measurements, satellite observations and an atmospheric transport model to derive accurate NH3 surface concentrations and dry deposition fluxes on a large scale.

A combination of NH3 satellite observations from IASI and the LOTOS-EUROS model is used to...